\begin{abstract}
Program authorship identification has significant applications in software forensic and malware analysis. 
In these areas, source code is unlikely to be present. 
In this paper, we propose a new mode that identify authorship on binary programs. We first introduce binary provenance features used to abstract binary programs,
and then propose our machine learning model. In this model, we take a supervised learning approach and treat functions as the unit for classification.
Finally, we verify our model on a large code repository and show that our model has 62\% accuracy on a data set with 44 authors.
\end{abstract}
